Annual model - Regions
Tanya Rogers
12/15/2021
library(dplyr)
#library(psych) #for pairs.panels, but could use other packages, e.g. GGalley
library(lavaan)
library(semPlot)
library(DiagrammeR)
library(tidyr)
library(ggplot2)Import data
combined=read.csv("data/annual_averages/annual_data_compiled_regions.csv",stringsAsFactors = F)
cnames=read.csv("analysis/column_names_region.csv", stringsAsFactors = F)
dsub=filter(combined, Year>=1975) %>% arrange(Region,Year)
focaldata=dsub[,cnames$Datacolumn]
fvars=cnames$Shortname
colnames(focaldata)=fvars
regions=unique(focaldata$region)
regionorder=c("West","North","South")
years=1975:2021
focaldata = focaldata %>%
mutate(tzoop=hcope+clad+mysid+pcope,
tzoop_c=hcope_c+clad_c+mysid_c+pcope_c,
tzoop_e=hcope_e+clad_e+mysid_e+pcope_e,
hzoop=hcope+clad,
hzoop_c=hcope_c+clad_c,
hzoop_e=hcope_e+clad_e,
pzoop=mysid+pcope,
pzoop_c=mysid_c+pcope_c,
pzoop_e=mysid_e+pcope_e)
fvars=c(fvars,"tzoop","tzoop_c","tzoop_e",
"hzoop","hzoop_c","hzoop_e",
"pzoop","pzoop_c","pzoop_e")
cnames=rbind(cnames,data.frame(Longname=NA,Shortname=c("tzoop","tzoop_c","tzoop_e",
"hzoop","hzoop_c","hzoop_e",
"pzoop","pzoop_c","pzoop_e"),
Diagramname=c("Total Zooplankton\nBiomass",
"Total Zooplankton\nAbundance",
"Total Zooplankton\nEnergy",
"Herbivorous Zooplankton\nBiomass",
"Herbivorous Zooplankton\nAbundance",
"Herbivorous Zooplankton\nEnergy",
"Predatory Zooplankton\nBiomass",
"Predatory Zooplankton\nAbundance",
"Predatory Zooplankton\nEnergy"),
Datacolumn=NA,Log="yes"))
#focal variables
varnames=c("temp","flow","nitrate","ammonia","dophos","chla","hcope","clad","amphi","pcope","mysid","potam","corbic","sside","estfish_bsot","estfish_bsmt","tzoop","tzoop_c","tzoop_e","hzoop","pzoop")
source("analysis/myLavaanPlot.r")Data prep
Log transform, scale
#log transform
logvars=fvars[cnames$Log=="yes"]
logtrans=function(x) {
x2=x[which(!is.na(x))]
if(any(x2==0)) {log(x+min(x2[which(x2>0)],na.rm=T))}
else {log(x)}
}
focaldatalog = focaldata %>%
mutate_at(logvars,logtrans)
#scale data
fdr0=focaldatalog
tvars=fvars[-(1:2)]
fdr=fdr0 %>% group_by(region) %>%
#lag
mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame()
#detrended data
fdr_dtr=fdr0 %>% group_by(region) %>%
#detrend
mutate_at(tvars,function(x) {
x<<-x
if(!all(is.na(x))) {
if((length(which(x==0))/length(x))<0.5) {
x2<<-x
x2[x2==0]=NA
res<<-residuals(lm(x2~years))
out=x
out[which(!is.na(x2))]=res
return(out)
} else {return(x)}
} else {return(x)}
}) %>%
#lag
mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame() Time series plots
Cross-correlation matrices
(only sig correlations shown… no correction for multiple comparisons)
Fish indices are not correlated in S and N!
Best predictor of fish
l_models = data.frame(l_model = c("tzoop","tzoop_c","tzoop_e",
"hzoop","hzoop+pzoop","pzoop",
"tzoop+amphi","tzoop_e+amphi",
"hcope+clad+mysid",
"hcope+clad+mysid+pcope",
"hcope+clad+mysid+pcope+rotif_m",
"hcope+mysid",
"hcope+mysid+pcope",
"hcope+mysid+amphi"))
#Midwater Trawl
westmods = purrr::map(l_models$l_model, ~lm(paste0("estfish_bsmt ~", .x),
data = filter(fdr_dtr,region=="West")))
names(westmods)=l_models$l_model
bbmle::AICctab(westmods,weights=T,delta=T)## dAICc df weight
## tzoop_e 0.0 3 0.1725
## tzoop_e+amphi 0.1 4 0.1611
## tzoop 0.2 3 0.1567
## tzoop+amphi 0.2 4 0.1526
## hcope+clad+mysid 0.9 5 0.1085
## hzoop 1.7 3 0.0730
## hzoop+pzoop 2.8 4 0.0436
## pzoop 3.3 3 0.0326
## hcope+clad+mysid+pcope 3.4 6 0.0316
## hcope+mysid 3.7 4 0.0265
## hcope+mysid+amphi 3.9 5 0.0241
## hcope+clad+mysid+pcope+rotif_m 6.1 7 0.0081
## hcope+mysid+pcope 6.2 5 0.0076
## tzoop_c 9.4 3 0.0015northmods = purrr::map(l_models$l_model, ~lm(paste0("estfish_bsmt ~", .x),
data = filter(fdr_dtr,region=="North")))
names(northmods)=l_models$l_model
bbmle::AICctab(northmods,weights=T,delta=T)## dAICc df weight
## tzoop+amphi 0.0 4 0.2172
## tzoop_e+amphi 0.4 4 0.1819
## tzoop 0.5 3 0.1652
## tzoop_e 1.1 3 0.1278
## pzoop 1.5 3 0.1042
## hcope+clad+mysid+pcope+rotif_m 2.6 7 0.0587
## hcope+clad+mysid 3.0 5 0.0487
## hcope+clad+mysid+pcope 3.7 6 0.0341
## hzoop+pzoop 3.9 4 0.0306
## hcope+mysid+amphi 5.7 5 0.0126
## hcope+mysid+pcope 6.3 5 0.0092
## hcope+mysid 6.4 4 0.0090
## tzoop_c 12.3 3 <0.001
## hzoop 12.7 3 <0.001southmods = purrr::map(l_models$l_model, ~lm(paste0("estfish_bsmt ~", .x),
data = filter(fdr_dtr,region=="South")))
names(southmods)=l_models$l_model
bbmle::AICctab(southmods,weights=T,delta=T)## dAICc df weight
## hzoop 0.0 3 0.1716
## tzoop 0.3 3 0.1481
## tzoop_e 0.3 3 0.1452
## tzoop_c 0.5 3 0.1316
## pzoop 0.9 3 0.1100
## hcope+clad+mysid 2.4 5 0.0521
## hzoop+pzoop 2.4 4 0.0512
## hcope+mysid 2.7 4 0.0451
## tzoop+amphi 2.8 4 0.0433
## tzoop_e+amphi 2.8 4 0.0424
## hcope+clad+mysid+pcope 4.0 6 0.0232
## hcope+mysid+pcope 4.8 5 0.0155
## hcope+mysid+amphi 5.3 5 0.0121
## hcope+clad+mysid+pcope+rotif_m 6.0 7 0.0087tzoop and tzoop_e do pretty well in all regions. tzoop_c and hzoop+pzoop does poorly in all regions. amphi seem relevant in North (for midwater trawl), which is also where they are most abundant.
l_modelsw = data.frame(l_model = c("tzoop",
"tzoop+marfish_bsmt"))
l_modelsns = data.frame(l_model = c("tzoop",
"tzoop+amphi",
"tzoop+amphi+sside",
"tzoop+sside",
"tzoop+corbic",
"tzoop+corbic+sside"))
#Midwater Trawl
westmods = purrr::map(l_modelsw$l_model, ~lm(paste0("estfish_bsmt ~", .x),
data = filter(fdr_dtr,region=="West")))
names(westmods)=l_modelsw$l_model
bbmle::AICctab(westmods,weights=T,delta=T)## dAICc df weight
## tzoop 0.0 3 0.74
## tzoop+marfish_bsmt 2.1 4 0.26northmods = purrr::map(l_modelsns$l_model, ~lm(paste0("estfish_bsmt ~", .x),
data = filter(fdr_dtr,region=="North")))
names(northmods)=l_modelsns$l_model
bbmle::AICctab(northmods,weights=T,delta=T)## dAICc df weight
## tzoop+amphi+sside 0.0 5 0.323
## tzoop+sside 0.3 4 0.279
## tzoop+corbic+sside 1.5 5 0.154
## tzoop+amphi 2.1 4 0.114
## tzoop 2.6 3 0.087
## tzoop+corbic 4.1 4 0.043southmods = purrr::map(l_modelsns$l_model, ~lm(paste0("estfish_bsmt ~", .x),
data = filter(fdr_dtr,region=="South")))
names(southmods)=l_modelsns$l_model
bbmle::AICctab(southmods,weights=T,delta=T)## dAICc df weight
## tzoop+corbic 0.0 4 0.454
## tzoop+corbic+sside 1.9 5 0.173
## tzoop 2.1 3 0.162
## tzoop+sside 2.5 4 0.128
## tzoop+amphi 4.5 4 0.047
## tzoop+amphi+sside 5.1 5 0.035SEM model
With and without detrending.
#west has no ssides, corbic
#1
# modwest='zoop=~hcope+mysid
# fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# fish~zoop+flow
# '
#2
# modwest='chla~potam+flow
# tzoop~chla+potam+flow
# estfish_bsmt~tzoop+flow
# estfish_bsot~tzoop+flow
# '
#3
# modwest='chla~potam+flow+temp+secchi
# tzoop~chla+potam+flow+temp+secchi
# amphi~chla+potam+flow+temp+secchi
# estfish_bsmt~tzoop+amphi+flow
# #estfish_bsot~tzoop+amphi+flow+temp+secchi
# amphi~~tzoop
# '
#4
modwest='chla~potam+flow+temp+secchi
hzoop~chla+potam+flow+temp+secchi
pzoop~chla+potam+flow+temp+secchi+hzoop
amphi~chla+potam+flow+temp+secchi
estfish_bsmt~hzoop+pzoop+amphi+flow+temp+secchi #+marfish_bsmt
amphi~~hzoop+pzoop
'
modfitwest=sem(modwest, data=filter(fdr,region=="West"))
modfitwest_dtr=sem(modwest, data=filter(fdr_dtr,region=="West"))
summary(modfitwest, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 39 iterations
##
## Optimization method NLMINB
## Number of free parameters 33
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 3.390
## Degrees of freedom 2
## P-value (Chi-square) 0.184
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam -0.361 0.148 -2.438 0.015 -0.361 -0.422
## flow 0.148 0.153 0.964 0.335 0.148 0.179
## temp 0.010 0.131 0.074 0.941 0.010 0.011
## secchi 0.126 0.196 0.640 0.522 0.126 0.124
## hzoop ~
## chla 0.650 0.098 6.649 0.000 0.650 0.659
## potam -0.161 0.098 -1.642 0.101 -0.161 -0.191
## flow 0.189 0.096 1.964 0.049 0.189 0.231
## temp 0.160 0.081 1.966 0.049 0.160 0.188
## secchi 0.113 0.122 0.923 0.356 0.113 0.113
## pzoop ~
## chla 0.541 0.127 4.270 0.000 0.541 0.529
## potam -0.060 0.090 -0.664 0.507 -0.060 -0.069
## flow -0.152 0.090 -1.695 0.090 -0.152 -0.180
## temp 0.116 0.076 1.535 0.125 0.116 0.133
## secchi 0.016 0.110 0.145 0.884 0.016 0.016
## hzoop 0.404 0.141 2.867 0.004 0.404 0.391
## amphi ~
## chla 0.050 0.143 0.352 0.725 0.050 0.047
## potam 0.298 0.143 2.080 0.038 0.298 0.324
## flow 0.377 0.140 2.682 0.007 0.377 0.423
## temp -0.376 0.119 -3.165 0.002 -0.376 -0.406
## secchi 0.458 0.178 2.568 0.010 0.458 0.422
## estfish_bsmt ~
## hzoop 0.044 0.218 0.203 0.839 0.044 0.038
## pzoop 0.600 0.198 3.033 0.002 0.600 0.529
## amphi 0.027 0.148 0.180 0.857 0.027 0.025
## flow 0.372 0.151 2.452 0.014 0.372 0.388
## temp 0.057 0.122 0.469 0.639 0.057 0.058
## secchi -0.155 0.173 -0.892 0.372 -0.155 -0.133
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .amphi -0.135 0.055 -2.433 0.015 -0.135 -0.417
## .pzoop ~~
## .amphi -0.111 0.045 -2.461 0.014 -0.111 -0.384
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.578 0.129 4.472 0.000 0.578 0.793
## .hzoop 0.221 0.049 4.472 0.000 0.221 0.311
## .pzoop 0.176 0.039 4.472 0.000 0.176 0.232
## .amphi 0.472 0.106 4.472 0.000 0.472 0.560
## .estfish_bsmt 0.397 0.089 4.472 0.000 0.397 0.407
##
## R-Square:
## Estimate
## chla 0.207
## hzoop 0.689
## pzoop 0.768
## amphi 0.440
## estfish_bsmt 0.593summary(modfitwest_dtr, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 32 iterations
##
## Optimization method NLMINB
## Number of free parameters 33
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 2.897
## Degrees of freedom 2
## P-value (Chi-square) 0.235
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam 0.037 0.157 0.236 0.814 0.037 0.039
## flow 0.321 0.178 1.799 0.072 0.321 0.342
## temp -0.287 0.166 -1.736 0.083 -0.287 -0.298
## secchi 0.466 0.234 1.994 0.046 0.466 0.388
## hzoop ~
## chla 0.654 0.112 5.817 0.000 0.654 0.654
## potam -0.107 0.112 -0.959 0.338 -0.107 -0.113
## flow 0.283 0.132 2.144 0.032 0.283 0.302
## temp 0.133 0.122 1.091 0.275 0.133 0.138
## secchi 0.240 0.174 1.374 0.169 0.240 0.199
## pzoop ~
## chla 0.445 0.127 3.517 0.000 0.445 0.509
## potam 0.037 0.094 0.397 0.691 0.037 0.045
## flow -0.089 0.115 -0.771 0.441 -0.089 -0.109
## temp 0.067 0.103 0.649 0.516 0.067 0.079
## secchi 0.107 0.148 0.722 0.471 0.107 0.101
## hzoop 0.297 0.131 2.267 0.023 0.297 0.339
## amphi ~
## chla 0.228 0.143 1.595 0.111 0.228 0.237
## potam 0.058 0.142 0.407 0.684 0.058 0.063
## flow 0.336 0.168 2.004 0.045 0.336 0.372
## temp -0.196 0.155 -1.265 0.206 -0.196 -0.211
## secchi 0.233 0.222 1.053 0.292 0.233 0.201
## estfish_bsmt ~
## hzoop 0.063 0.179 0.351 0.726 0.063 0.061
## pzoop 0.481 0.194 2.482 0.013 0.481 0.410
## amphi 0.064 0.142 0.453 0.650 0.064 0.060
## flow 0.426 0.174 2.445 0.014 0.426 0.442
## temp 0.008 0.141 0.057 0.955 0.008 0.008
## secchi -0.055 0.217 -0.252 0.801 -0.055 -0.044
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .amphi -0.199 0.085 -2.349 0.019 -0.199 -0.400
## .pzoop ~~
## .amphi -0.122 0.063 -1.944 0.052 -0.122 -0.296
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.775 0.173 4.472 0.000 0.775 0.840
## .hzoop 0.392 0.088 4.472 0.000 0.392 0.425
## .pzoop 0.269 0.060 4.472 0.000 0.269 0.381
## .amphi 0.633 0.141 4.472 0.000 0.633 0.739
## .estfish_bsmt 0.537 0.120 4.472 0.000 0.537 0.551
##
## R-Square:
## Estimate
## chla 0.160
## hzoop 0.575
## pzoop 0.619
## amphi 0.261
## estfish_bsmt 0.449# par(mfrow=c(1,2))
# semPaths(modfitwest, "std", edge.label.cex = 1, residuals = F)
# semPaths(modfitwest, "par", edge.label.cex = 1, residuals = F)
labelswest <- createLabels(modfitwest, cnames)
# residuals(modfitwest)
# modificationindices(modfitwest)#no potam
#1
# modnorth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
# modnorth='zoop=~clad
# zoop~chla+corbic+potam+flow
# chla~corbic+potam+flow
# estfish_bsmt~zoop+flow+sside+chla
# estfish_bsot~zoop+flow+sside+chla
# '
#2
# modnorth='chla~corbic+potam+flow
# tzoop~chla+corbic+potam+flow
# estfish_bsmt~tzoop+flow+chla
# estfish_bsot~tzoop+flow
# '
#3
# modnorth='chla~corbic+potam+flow+temp+secchi
# tzoop~chla+corbic+potam+flow+temp+secchi
# amphi~chla+corbic+potam+flow+temp+secchi
# estfish_bsmt~tzoop+amphi+flow+temp+secchi+chla+sside
# #estfish_bsot~tzoop+amphi+flow+temp+secchi+sside
# amphi~~tzoop
# '
#4
modnorth='chla~corbic+flow+temp+secchi
hzoop~chla+corbic+flow+temp+secchi
pzoop~chla+corbic+flow+temp+secchi+hzoop
amphi~chla+corbic+flow+temp+secchi
estfish_bsmt~hzoop+pzoop+amphi+flow+temp+secchi+chla+sside
amphi~~hzoop+pzoop
'
modfitnorth=sem(modnorth, data=filter(fdr,region=="North"))
modfitnorth_dtr=sem(modnorth, data=filter(fdr_dtr,region=="North"))
summary(modfitnorth, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 40 iterations
##
## Optimization method NLMINB
## Number of free parameters 35
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 6.981
## Degrees of freedom 5
## P-value (Chi-square) 0.222
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.345 0.127 2.713 0.007 0.345 0.407
## flow 0.106 0.146 0.723 0.469 0.106 0.124
## temp 0.134 0.138 0.972 0.331 0.134 0.153
## secchi -0.168 0.140 -1.202 0.229 -0.168 -0.170
## hzoop ~
## chla 0.062 0.112 0.552 0.581 0.062 0.062
## corbic 0.262 0.098 2.669 0.008 0.262 0.307
## flow -0.226 0.105 -2.162 0.031 -0.226 -0.263
## temp 0.321 0.099 3.238 0.001 0.321 0.363
## secchi -0.489 0.101 -4.848 0.000 -0.489 -0.493
## pzoop ~
## chla 0.642 0.093 6.880 0.000 0.642 0.574
## corbic 0.345 0.088 3.908 0.000 0.345 0.363
## flow -0.338 0.092 -3.686 0.000 -0.338 -0.353
## temp 0.184 0.092 1.997 0.046 0.184 0.187
## secchi -0.105 0.105 -0.994 0.320 -0.105 -0.095
## hzoop 0.047 0.131 0.362 0.717 0.047 0.043
## amphi ~
## chla 0.340 0.137 2.471 0.013 0.340 0.313
## corbic 0.388 0.120 3.224 0.001 0.388 0.420
## flow -0.041 0.128 -0.323 0.747 -0.041 -0.045
## temp -0.505 0.122 -4.153 0.000 -0.505 -0.528
## secchi 0.160 0.124 1.295 0.195 0.160 0.149
## estfish_bsmt ~
## hzoop -0.202 0.194 -1.040 0.298 -0.202 -0.181
## pzoop -0.186 0.225 -0.827 0.408 -0.186 -0.185
## amphi 0.313 0.159 1.968 0.049 0.313 0.302
## flow 0.110 0.142 0.776 0.438 0.110 0.114
## temp 0.518 0.176 2.944 0.003 0.518 0.523
## secchi 0.121 0.160 0.759 0.448 0.121 0.109
## chla 0.644 0.209 3.083 0.002 0.644 0.572
## sside -0.167 0.159 -1.048 0.295 -0.167 -0.134
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .amphi -0.070 0.053 -1.335 0.182 -0.070 -0.216
## .pzoop ~~
## .amphi 0.044 0.042 1.047 0.295 0.044 0.164
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.528 0.118 4.472 0.000 0.528 0.692
## .hzoop 0.266 0.059 4.472 0.000 0.266 0.344
## .pzoop 0.183 0.041 4.472 0.000 0.183 0.191
## .amphi 0.399 0.089 4.472 0.000 0.399 0.443
## .estfish_bsmt 0.433 0.097 4.472 0.000 0.433 0.448
##
## R-Square:
## Estimate
## chla 0.308
## hzoop 0.656
## pzoop 0.809
## amphi 0.557
## estfish_bsmt 0.552summary(modfitnorth_dtr, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 32 iterations
##
## Optimization method NLMINB
## Number of free parameters 35
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 7.226
## Degrees of freedom 5
## P-value (Chi-square) 0.204
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.426 0.170 2.509 0.012 0.426 0.421
## flow 0.079 0.177 0.449 0.653 0.079 0.082
## temp 0.177 0.164 1.077 0.281 0.177 0.178
## secchi -0.272 0.158 -1.715 0.086 -0.272 -0.270
## hzoop ~
## chla 0.102 0.123 0.828 0.408 0.102 0.116
## corbic 0.212 0.142 1.499 0.134 0.212 0.240
## flow -0.299 0.138 -2.174 0.030 -0.299 -0.354
## temp 0.316 0.129 2.449 0.014 0.316 0.365
## secchi -0.300 0.127 -2.358 0.018 -0.300 -0.342
## pzoop ~
## chla 0.663 0.095 6.967 0.000 0.663 0.635
## corbic 0.378 0.112 3.377 0.001 0.378 0.358
## flow -0.406 0.112 -3.623 0.000 -0.406 -0.401
## temp 0.193 0.106 1.813 0.070 0.193 0.186
## secchi -0.035 0.105 -0.336 0.737 -0.035 -0.033
## hzoop 0.019 0.122 0.157 0.875 0.019 0.016
## amphi ~
## chla 0.295 0.111 2.666 0.008 0.295 0.307
## corbic 0.458 0.128 3.582 0.000 0.458 0.471
## flow -0.062 0.124 -0.502 0.616 -0.062 -0.067
## temp -0.394 0.116 -3.381 0.001 -0.394 -0.413
## secchi -0.031 0.115 -0.268 0.789 -0.031 -0.032
## estfish_bsmt ~
## hzoop -0.041 0.141 -0.290 0.772 -0.041 -0.037
## pzoop 0.068 0.178 0.380 0.704 0.068 0.072
## amphi 0.261 0.150 1.743 0.081 0.261 0.256
## flow 0.272 0.133 2.042 0.041 0.272 0.286
## temp 0.494 0.148 3.349 0.001 0.494 0.508
## secchi -0.031 0.111 -0.277 0.782 -0.031 -0.031
## chla 0.354 0.166 2.132 0.033 0.354 0.361
## sside -0.175 0.112 -1.560 0.119 -0.175 -0.164
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .amphi -0.069 0.068 -1.014 0.310 -0.069 -0.162
## .pzoop ~~
## .amphi 0.057 0.051 1.110 0.267 0.057 0.176
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.776 0.173 4.472 0.000 0.776 0.799
## .hzoop 0.468 0.105 4.472 0.000 0.468 0.631
## .pzoop 0.276 0.062 4.472 0.000 0.276 0.261
## .amphi 0.380 0.085 4.472 0.000 0.380 0.424
## .estfish_bsmt 0.386 0.086 4.472 0.000 0.386 0.413
##
## R-Square:
## Estimate
## chla 0.201
## hzoop 0.369
## pzoop 0.739
## amphi 0.576
## estfish_bsmt 0.587# par(mfrow=c(1,2))
# semPaths(modfitnorth, "std", edge.label.cex = 1, residuals = F)
# semPaths(modfitnorth, "par", edge.label.cex = 1, residuals = F)
labelsnorth <- createLabels(modfitnorth, cnames)
# residuals(modfitnorth)
# modificationindices(modfitnorth)#no potam
#1
# modsouth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+corbic+flow
# chla~corbic+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
#2
# modsouth='chla~corbic+flow
# tzoop~chla+corbic+flow
# estfish_bsmt~tzoop+flow+corbic+sside
# estfish_bsot~tzoop+flow+corbic+sside
# '
#3
# modsouth='chla~corbic+flow+temp+secchi
# tzoop~chla+corbic+flow+temp+secchi
# amphi~chla+corbic+flow+temp+secchi
# estfish_bsmt~tzoop+amphi+flow+temp+secchi+corbic+sside
# #estfish_bsot~tzoop+amphi+flow+temp+secchi+corbic+sside
# amphi~~tzoop
# '
#4
modsouth='chla~corbic+flow+temp+secchi
hzoop~chla+corbic+flow+temp+secchi
pzoop~chla+corbic+flow+temp+secchi+hzoop
amphi~chla+corbic+flow+temp+secchi
estfish_bsmt~hzoop+pzoop+amphi+flow+temp+secchi+corbic+sside
amphi~~hzoop+pzoop
'
modfitsouth=sem(modsouth, data=filter(fdr,region=="South"))
modfitsouth_dtr=sem(modsouth, data=filter(fdr_dtr,region=="South"))
summary(modfitsouth, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 32 iterations
##
## Optimization method NLMINB
## Number of free parameters 35
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 8.954
## Degrees of freedom 5
## P-value (Chi-square) 0.111
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.158 0.147 1.076 0.282 0.158 0.155
## flow 0.159 0.149 1.067 0.286 0.159 0.163
## temp 0.440 0.148 2.979 0.003 0.440 0.438
## secchi -0.318 0.152 -2.092 0.036 -0.318 -0.286
## hzoop ~
## chla 0.473 0.139 3.403 0.001 0.473 0.481
## corbic 0.301 0.131 2.295 0.022 0.301 0.299
## flow -0.097 0.133 -0.735 0.462 -0.097 -0.101
## temp 0.194 0.144 1.352 0.176 0.194 0.197
## secchi 0.091 0.141 0.644 0.519 0.091 0.083
## pzoop ~
## chla 0.513 0.123 4.153 0.000 0.513 0.547
## corbic -0.051 0.109 -0.468 0.640 -0.051 -0.053
## flow 0.045 0.104 0.433 0.665 0.045 0.049
## temp 0.246 0.115 2.141 0.032 0.246 0.261
## secchi -0.135 0.111 -1.216 0.224 -0.135 -0.129
## hzoop 0.111 0.124 0.894 0.372 0.111 0.116
## amphi ~
## chla 0.062 0.146 0.425 0.671 0.062 0.084
## corbic -0.053 0.138 -0.387 0.699 -0.053 -0.070
## flow -0.115 0.139 -0.827 0.408 -0.115 -0.159
## temp 0.136 0.151 0.904 0.366 0.136 0.183
## secchi 0.078 0.148 0.526 0.599 0.078 0.095
## estfish_bsmt ~
## hzoop -0.325 0.168 -1.938 0.053 -0.325 -0.329
## pzoop 0.164 0.186 0.883 0.377 0.164 0.158
## amphi 0.007 0.167 0.045 0.964 0.007 0.006
## flow -0.147 0.148 -0.998 0.319 -0.147 -0.155
## temp 0.002 0.171 0.014 0.989 0.002 0.002
## secchi -0.488 0.229 -2.130 0.033 -0.488 -0.453
## corbic 0.506 0.174 2.917 0.004 0.506 0.509
## sside 0.114 0.292 0.390 0.697 0.114 0.090
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .amphi 0.054 0.077 0.697 0.486 0.054 0.111
## .pzoop ~~
## .amphi -0.008 0.059 -0.142 0.887 -0.008 -0.022
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.596 0.133 4.472 0.000 0.596 0.573
## .hzoop 0.460 0.103 4.472 0.000 0.460 0.458
## .pzoop 0.282 0.063 4.472 0.000 0.282 0.308
## .amphi 0.507 0.113 4.472 0.000 0.507 0.890
## .estfish_bsmt 0.556 0.124 4.472 0.000 0.556 0.567
##
## R-Square:
## Estimate
## chla 0.427
## hzoop 0.542
## pzoop 0.692
## amphi 0.110
## estfish_bsmt 0.433summary(modfitsouth_dtr, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 25 iterations
##
## Optimization method NLMINB
## Number of free parameters 35
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 7.040
## Degrees of freedom 5
## P-value (Chi-square) 0.218
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.064 0.158 0.408 0.683 0.064 0.063
## flow 0.208 0.175 1.189 0.234 0.208 0.213
## temp 0.450 0.173 2.599 0.009 0.450 0.447
## secchi 0.004 0.151 0.025 0.980 0.004 0.004
## hzoop ~
## chla 0.417 0.132 3.162 0.002 0.417 0.415
## corbic 0.262 0.132 1.988 0.047 0.262 0.257
## flow -0.108 0.149 -0.727 0.467 -0.108 -0.110
## temp 0.196 0.156 1.257 0.209 0.196 0.194
## secchi 0.102 0.126 0.810 0.418 0.102 0.102
## pzoop ~
## chla 0.519 0.136 3.807 0.000 0.519 0.511
## corbic -0.130 0.128 -1.020 0.308 -0.130 -0.126
## flow 0.065 0.138 0.471 0.638 0.065 0.066
## temp 0.281 0.147 1.908 0.056 0.281 0.275
## secchi 0.076 0.118 0.644 0.520 0.076 0.075
## hzoop 0.117 0.146 0.798 0.425 0.117 0.115
## amphi ~
## chla 0.085 0.122 0.695 0.487 0.085 0.113
## corbic -0.007 0.122 -0.060 0.952 -0.007 -0.010
## flow -0.114 0.138 -0.829 0.407 -0.114 -0.155
## temp 0.156 0.145 1.082 0.279 0.156 0.206
## secchi -0.044 0.117 -0.373 0.709 -0.044 -0.058
## estfish_bsmt ~
## hzoop -0.335 0.172 -1.943 0.052 -0.335 -0.344
## pzoop 0.126 0.165 0.760 0.447 0.126 0.131
## amphi 0.012 0.193 0.062 0.950 0.012 0.009
## flow -0.169 0.170 -0.992 0.321 -0.169 -0.177
## temp -0.002 0.189 -0.009 0.993 -0.002 -0.002
## secchi -0.258 0.164 -1.576 0.115 -0.258 -0.264
## corbic 0.478 0.172 2.786 0.005 0.478 0.482
## sside 0.074 0.197 0.375 0.708 0.074 0.063
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .amphi 0.067 0.089 0.755 0.450 0.067 0.120
## .pzoop ~~
## .amphi 0.004 0.081 0.053 0.958 0.004 0.008
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.863 0.193 4.472 0.000 0.863 0.835
## .hzoop 0.599 0.134 4.472 0.000 0.599 0.576
## .pzoop 0.513 0.115 4.472 0.000 0.513 0.482
## .amphi 0.516 0.115 4.472 0.000 0.516 0.879
## .estfish_bsmt 0.762 0.170 4.472 0.000 0.762 0.776
##
## R-Square:
## Estimate
## chla 0.165
## hzoop 0.424
## pzoop 0.518
## amphi 0.121
## estfish_bsmt 0.224# par(mfrow=c(1,2))
# semPaths(modfitsouth, "std", edge.label.cex = 1, residuals = F)
# semPaths(modfitsouth, "par", edge.label.cex = 1, residuals = F)
labelssouth <- createLabels(modfitsouth, cnames)
# residuals(modfitsouth)
# modificationindices(modfitsouth)Nice plots
Original units
West
North
South
Detrended
West
North
South
Older SEM model (h and p zoop split)
With and without detrending.
#west has no ssides, corbic
modwest='chla~potam
potam~flow
hzoop~chla+potam+flow
pzoop~chla+potam+flow+hzoop
estfish_bsmt~hzoop+pzoop+flow
estfish_bsot~hzoop+pzoop+flow
'
modfitwest=sem(modwest, data=filter(fdr,region=="West"))
modfitwest_dtr=sem(modwest, data=filter(fdr_dtr,region=="West"))
summary(modfitwest, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 33 iterations
##
## Optimization method NLMINB
## Number of free parameters 22
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 5.981
## Degrees of freedom 5
## P-value (Chi-square) 0.308
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam -0.370 0.122 -3.036 0.002 -0.370 -0.433
## potam ~
## flow -0.430 0.137 -3.133 0.002 -0.430 -0.444
## hzoop ~
## chla 0.665 0.103 6.463 0.000 0.665 0.678
## potam -0.142 0.096 -1.478 0.139 -0.142 -0.170
## flow 0.085 0.086 0.987 0.323 0.085 0.104
## pzoop ~
## chla 0.498 0.127 3.924 0.000 0.498 0.481
## potam -0.057 0.085 -0.673 0.501 -0.057 -0.065
## flow -0.209 0.075 -2.793 0.005 -0.209 -0.244
## hzoop 0.476 0.136 3.491 0.000 0.476 0.451
## estfish_bsmt ~
## hzoop 0.040 0.218 0.182 0.856 0.040 0.034
## pzoop 0.611 0.196 3.112 0.002 0.611 0.556
## flow 0.434 0.111 3.911 0.000 0.434 0.460
## estfish_bsot ~
## hzoop 0.144 0.220 0.655 0.512 0.144 0.124
## pzoop 0.517 0.198 2.609 0.009 0.517 0.471
## flow 0.410 0.112 3.662 0.000 0.410 0.436
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt ~~
## .estfish_bsot 0.062 0.066 0.946 0.344 0.062 0.151
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.592 0.132 4.472 0.000 0.592 0.813
## .potam 0.801 0.179 4.472 0.000 0.801 0.803
## .hzoop 0.251 0.056 4.472 0.000 0.251 0.358
## .pzoop 0.187 0.042 4.472 0.000 0.187 0.239
## .estfish_bsmt 0.407 0.091 4.472 0.000 0.407 0.430
## .estfish_bsot 0.414 0.093 4.472 0.000 0.414 0.440
##
## R-Square:
## Estimate
## chla 0.187
## potam 0.197
## hzoop 0.642
## pzoop 0.761
## estfish_bsmt 0.570
## estfish_bsot 0.560summary(modfitwest_dtr, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 26 iterations
##
## Optimization method NLMINB
## Number of free parameters 22
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 8.863
## Degrees of freedom 5
## P-value (Chi-square) 0.115
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam -0.061 0.150 -0.409 0.682 -0.061 -0.065
## potam ~
## flow -0.423 0.141 -2.996 0.003 -0.423 -0.428
## hzoop ~
## chla 0.669 0.109 6.121 0.000 0.669 0.681
## potam -0.048 0.115 -0.420 0.675 -0.048 -0.052
## flow 0.138 0.113 1.217 0.223 0.138 0.150
## pzoop ~
## chla 0.417 0.121 3.446 0.001 0.417 0.468
## potam 0.068 0.092 0.739 0.460 0.068 0.080
## flow -0.163 0.092 -1.772 0.076 -0.163 -0.195
## hzoop 0.347 0.126 2.752 0.006 0.347 0.382
## estfish_bsmt ~
## hzoop 0.052 0.176 0.296 0.767 0.052 0.051
## pzoop 0.475 0.192 2.468 0.014 0.475 0.424
## flow 0.473 0.124 3.816 0.000 0.473 0.505
## estfish_bsot ~
## hzoop 0.152 0.175 0.871 0.384 0.152 0.148
## pzoop 0.429 0.191 2.244 0.025 0.429 0.379
## flow 0.440 0.123 3.578 0.000 0.440 0.466
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt ~~
## .estfish_bsot 0.080 0.086 0.938 0.348 0.080 0.150
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.919 0.205 4.472 0.000 0.919 0.996
## .potam 0.836 0.187 4.472 0.000 0.836 0.817
## .hzoop 0.439 0.098 4.472 0.000 0.439 0.494
## .pzoop 0.278 0.062 4.472 0.000 0.278 0.380
## .estfish_bsmt 0.540 0.121 4.472 0.000 0.540 0.586
## .estfish_bsot 0.533 0.119 4.472 0.000 0.533 0.568
##
## R-Square:
## Estimate
## chla 0.004
## potam 0.183
## hzoop 0.506
## pzoop 0.620
## estfish_bsmt 0.414
## estfish_bsot 0.432labelswest <- createLabels(modfitwest, cnames)
# residuals(modfitwest)
# modificationindices(modfitwest)modnorth='chla~corbic+potam
potam~flow
corbic~flow
hzoop~chla+corbic+potam+flow
pzoop~chla+corbic+potam+flow+hzoop
estfish_bsmt~hzoop+pzoop+flow
estfish_bsot~hzoop+pzoop+flow
'
modfitnorth=sem(modnorth, data=filter(fdr,region=="North"))
modfitnorth_dtr=sem(modnorth, data=filter(fdr_dtr,region=="North"))
summary(modfitnorth, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 24 iterations
##
## Optimization method NLMINB
## Number of free parameters 27
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 13.130
## Degrees of freedom 8
## P-value (Chi-square) 0.107
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.363 0.118 3.086 0.002 0.363 0.435
## potam -0.130 0.120 -1.083 0.279 -0.130 -0.153
## potam ~
## flow -0.498 0.134 -3.708 0.000 -0.498 -0.506
## corbic ~
## flow 0.398 0.146 2.721 0.007 0.398 0.395
## hzoop ~
## chla 0.283 0.158 1.796 0.072 0.283 0.275
## corbic 0.319 0.138 2.316 0.021 0.319 0.370
## potam 0.086 0.138 0.626 0.531 0.086 0.098
## flow -0.282 0.143 -1.971 0.049 -0.282 -0.325
## pzoop ~
## chla 0.673 0.099 6.826 0.000 0.673 0.593
## corbic 0.332 0.088 3.762 0.000 0.332 0.350
## potam 0.041 0.083 0.489 0.625 0.041 0.042
## flow -0.346 0.090 -3.843 0.000 -0.346 -0.363
## hzoop 0.222 0.095 2.336 0.020 0.222 0.202
## estfish_bsmt ~
## hzoop -0.175 0.183 -0.953 0.341 -0.175 -0.157
## pzoop 0.586 0.165 3.544 0.000 0.586 0.582
## flow 0.258 0.129 1.999 0.046 0.258 0.268
## estfish_bsot ~
## hzoop 0.167 0.178 0.936 0.349 0.167 0.152
## pzoop 0.439 0.161 2.734 0.006 0.439 0.443
## flow 0.231 0.125 1.842 0.065 0.231 0.244
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt ~~
## .estfish_bsot 0.005 0.104 0.049 0.961 0.005 0.008
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.563 0.126 4.472 0.000 0.563 0.761
## .potam 0.754 0.169 4.472 0.000 0.754 0.744
## .corbic 0.893 0.200 4.472 0.000 0.893 0.844
## .hzoop 0.559 0.125 4.472 0.000 0.559 0.712
## .pzoop 0.203 0.045 4.472 0.000 0.203 0.213
## .estfish_bsmt 0.679 0.152 4.472 0.000 0.679 0.700
## .estfish_bsot 0.640 0.143 4.472 0.000 0.640 0.682
##
## R-Square:
## Estimate
## chla 0.239
## potam 0.256
## corbic 0.156
## hzoop 0.288
## pzoop 0.787
## estfish_bsmt 0.300
## estfish_bsot 0.318summary(modfitnorth_dtr, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 23 iterations
##
## Optimization method NLMINB
## Number of free parameters 27
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 7.125
## Degrees of freedom 8
## P-value (Chi-square) 0.523
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.353 0.152 2.325 0.020 0.353 0.349
## potam 0.028 0.141 0.195 0.845 0.028 0.029
## potam ~
## flow -0.421 0.148 -2.841 0.004 -0.421 -0.410
## corbic ~
## flow 0.371 0.139 2.657 0.008 0.371 0.387
## hzoop ~
## chla 0.214 0.131 1.628 0.103 0.214 0.242
## corbic 0.067 0.143 0.472 0.637 0.067 0.076
## potam 0.006 0.127 0.047 0.963 0.006 0.007
## flow -0.373 0.140 -2.666 0.008 -0.373 -0.437
## pzoop ~
## chla 0.672 0.094 7.115 0.000 0.672 0.632
## corbic 0.355 0.100 3.551 0.000 0.355 0.329
## potam 0.121 0.089 1.368 0.171 0.121 0.121
## flow -0.414 0.106 -3.916 0.000 -0.414 -0.403
## hzoop 0.095 0.110 0.858 0.391 0.095 0.078
## estfish_bsmt ~
## hzoop 0.116 0.159 0.730 0.466 0.116 0.103
## pzoop 0.544 0.127 4.286 0.000 0.544 0.580
## flow 0.375 0.131 2.875 0.004 0.375 0.389
## estfish_bsot ~
## hzoop -0.037 0.190 -0.195 0.845 -0.037 -0.032
## pzoop 0.373 0.151 2.464 0.014 0.373 0.395
## flow 0.176 0.155 1.131 0.258 0.176 0.181
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt ~~
## .estfish_bsot 0.072 0.112 0.648 0.517 0.072 0.103
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.854 0.191 4.472 0.000 0.854 0.880
## .potam 0.911 0.204 4.472 0.000 0.911 0.832
## .corbic 0.806 0.180 4.472 0.000 0.806 0.850
## .hzoop 0.588 0.131 4.472 0.000 0.588 0.781
## .pzoop 0.286 0.064 4.472 0.000 0.286 0.260
## .estfish_bsmt 0.590 0.132 4.472 0.000 0.590 0.611
## .estfish_bsot 0.836 0.187 4.472 0.000 0.836 0.855
##
## R-Square:
## Estimate
## chla 0.120
## potam 0.168
## corbic 0.150
## hzoop 0.219
## pzoop 0.740
## estfish_bsmt 0.389
## estfish_bsot 0.145labelsnorth <- createLabels(modfitnorth, cnames)
# residuals(modfitnorth)
# modificationindices(modfitnorth)#no potam
modsouth='chla~corbic
corbic~flow
hzoop~chla+corbic+flow
pzoop~chla+corbic+flow+hzoop
estfish_bsmt~hzoop+pzoop+flow+corbic
estfish_bsot~hzoop+pzoop+flow+corbic
'
modfitsouth=sem(modsouth, data=filter(fdr,region=="South"))
modfitsouth_dtr=sem(modsouth, data=filter(fdr_dtr,region=="South"))
summary(modfitsouth, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 24 iterations
##
## Optimization method NLMINB
## Number of free parameters 24
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 0.105
## Degrees of freedom 3
## P-value (Chi-square) 0.991
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.442 0.146 3.027 0.002 0.442 0.432
## corbic ~
## flow 0.301 0.143 2.101 0.036 0.301 0.315
## hzoop ~
## chla 0.526 0.120 4.393 0.000 0.526 0.536
## corbic 0.329 0.128 2.572 0.010 0.329 0.327
## flow -0.209 0.111 -1.883 0.060 -0.209 -0.218
## pzoop ~
## chla 0.642 0.120 5.359 0.000 0.642 0.685
## corbic 0.015 0.114 0.135 0.893 0.015 0.016
## flow -0.047 0.095 -0.497 0.619 -0.047 -0.052
## hzoop 0.151 0.130 1.164 0.244 0.151 0.159
## estfish_bsmt ~
## hzoop -0.342 0.179 -1.913 0.056 -0.342 -0.346
## pzoop 0.318 0.171 1.854 0.064 0.318 0.308
## flow -0.065 0.134 -0.489 0.625 -0.065 -0.069
## corbic 0.563 0.161 3.496 0.000 0.563 0.568
## estfish_bsot ~
## hzoop 0.043 0.173 0.249 0.803 0.043 0.045
## pzoop 0.142 0.166 0.854 0.393 0.142 0.143
## flow -0.132 0.130 -1.017 0.309 -0.132 -0.145
## corbic 0.457 0.156 2.928 0.003 0.457 0.480
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt ~~
## .estfish_bsot -0.027 0.102 -0.268 0.789 -0.027 -0.042
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.846 0.189 4.472 0.000 0.846 0.814
## .corbic 0.894 0.200 4.472 0.000 0.894 0.901
## .hzoop 0.485 0.108 4.472 0.000 0.485 0.484
## .pzoop 0.328 0.073 4.472 0.000 0.328 0.359
## .estfish_bsmt 0.662 0.148 4.472 0.000 0.662 0.679
## .estfish_bsot 0.622 0.139 4.472 0.000 0.622 0.691
##
## R-Square:
## Estimate
## chla 0.186
## corbic 0.099
## hzoop 0.516
## pzoop 0.641
## estfish_bsmt 0.321
## estfish_bsot 0.309summary(modfitsouth_dtr, standardized=T, rsq=T)## lavaan 0.6-4 ended normally after 19 iterations
##
## Optimization method NLMINB
## Number of free parameters 24
##
## Used Total
## Number of observations 40 47
##
## Estimator ML
## Model Fit Test Statistic 0.126
## Degrees of freedom 3
## P-value (Chi-square) 0.988
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.151 0.159 0.951 0.341 0.151 0.149
## corbic ~
## flow 0.238 0.147 1.614 0.107 0.238 0.247
## hzoop ~
## chla 0.483 0.125 3.857 0.000 0.483 0.484
## corbic 0.312 0.131 2.371 0.018 0.312 0.307
## flow -0.237 0.125 -1.892 0.059 -0.237 -0.242
## pzoop ~
## chla 0.579 0.139 4.174 0.000 0.579 0.572
## corbic -0.094 0.133 -0.707 0.480 -0.094 -0.091
## flow -0.085 0.123 -0.686 0.493 -0.085 -0.086
## hzoop 0.186 0.149 1.245 0.213 0.186 0.184
## estfish_bsmt ~
## hzoop -0.340 0.173 -1.967 0.049 -0.340 -0.350
## pzoop 0.093 0.158 0.590 0.555 0.093 0.097
## flow -0.110 0.146 -0.751 0.452 -0.110 -0.116
## corbic 0.415 0.160 2.597 0.009 0.415 0.420
## estfish_bsot ~
## hzoop 0.044 0.171 0.260 0.795 0.044 0.046
## pzoop 0.027 0.156 0.175 0.861 0.027 0.029
## flow -0.172 0.145 -1.187 0.235 -0.172 -0.183
## corbic 0.366 0.158 2.312 0.021 0.366 0.375
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_bsmt ~~
## .estfish_bsot -0.087 0.128 -0.676 0.499 -0.087 -0.107
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 1.011 0.226 4.472 0.000 1.011 0.978
## .corbic 0.939 0.210 4.472 0.000 0.939 0.939
## .hzoop 0.635 0.142 4.472 0.000 0.635 0.615
## .pzoop 0.567 0.127 4.472 0.000 0.567 0.535
## .estfish_bsmt 0.814 0.182 4.472 0.000 0.814 0.834
## .estfish_bsot 0.797 0.178 4.472 0.000 0.797 0.840
##
## R-Square:
## Estimate
## chla 0.022
## corbic 0.061
## hzoop 0.385
## pzoop 0.465
## estfish_bsmt 0.166
## estfish_bsot 0.160labelssouth <- createLabels(modfitsouth, cnames)
# residuals(modfitsouth)
# modificationindices(modfitsouth)Nice plots
Original units
West
North
South
Detrended
West
North
South